Satellite Oceanographic Data Processing and Analysis: Correlation Between Nino 3.4 Sea Surface Temperature & Sea Surface High and Wind

Jen-Yang Lin; Ming-Chung Tang; Febrianto Wijaya

doi:10.52562/injoes.v2i2.428

Authors

Jen-Yang Lin Department of Oceanography, National Sun Yat-sen University, Taiwan
Ming-Chung Tang Department of Marine Environmental Informatics, National Taiwan Ocean University, Taiwan
Febrianto Wijaya Graduate Institute of Hydrological & Oceanic Sciences, National central University, Taiwan

DOI:

https://doi.org/10.52562/injoes.v2i2.428

Keywords:

ENSO, Nino 3.4, SST, SSH, Wind

Abstract

El Nino Southern Oscillation (ENSO) is an irregular climate oscillation induced by sea surface temperature anomalies (SSTA) in the Equatorial Pacific Ocean. An anomalous warming in this area is known as El Nino, while an anomalous cooling bears the name of La Nina. The objectives of this study are to: reproduce Oceanic Nino Index (ONI) based on MW OISST; produce Nino Region 3.4 wind and Sea Surface High (SSH); analyze the correlation between SST and Wind & SSH; discuss Typhoon Soudelor based on SST, Wind, and SSH; and analyze the correlation of El Nino and Precipitation in specific area. MWOI-SST was used to produce monthly mean SST over Nino 3.4 region from January 1998 – May 2020. Monthly wind data was obtained from QuickScat. Daily Sea Surface High (SSH) was obtained from Copernicus Marine Environment Monitoring Service (CMEMS). Daily precipitation from TRMM 3B42 over Bandung City (Indonesia) was used to assess the correlation between El Nino and precipitation in specific area. The results show that in Nino 3.4 region, 2015 is the hottest year during 1998-2020 period with average SST of 28.5oC, and 1999 is the coldest year with average SST of 25.7oC. The result shows that the MWOI-SST Ocean Nino Index has very strong correlation with ERSST.v5 with coefficient of correlation is 0.92 and RMSE is 0.36oC. the wind speed of Nino 3.4 region is range from 5.23 m/s to 7.97 m/s. Unlike Sea Surface Temperature (SST), annual average wind speed is more stable with monthly variation. The wind speed is observed high in the beginning and the end of years. Sea Surface Height (SSH) over Nino 3.4 region varied from 65.8 cm to 106.8 cm. 2015 is the highest SSH with annual average of 96 cm, whereas 1999 is the lowest SSH with annual average of 71.4 cm. It is observed that Sea Surface Temperature (SST) has negative correlation with wind speed with coefficient of correlation of 0.28. Conversely, Sea Surface Temperature (SST) over Nino 3.4 region has positive correlation with Sea Surface High (SSH) with coefficient of correlation of 0.30. which mean the higher temperature, the higher Sea Surface Height. During the passage of Typhoon Soudelor, there is evident cool trail along its track with rightward bias. We can assume that the decreasing precipitation in Bandung City might be affected by strong El Nino occurrence in 2015.

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References

Banholzer, S., & Donner, S. (2014). The influence of different El Niño types on global average temperature. Geophysical Research Letters, 41(6), 2093-2099. https://doi.org/10.1002/2014GL059520

Casey, K. S., & Adamec, D. (2002). Sea surface temperature and sea surface height variability in the North Pacific Ocean from 1993 to 1999. Journal of Geophysical Research: Oceans, 107(C8), 14-1. https://doi.org/10.1029/2001JC001060

Church, J. A., & White, N. J. (2006). A 20th century acceleration in global sea?level rise. Geophysical research letters, 33(1). https://doi.org/10.1029/2005GL024826

Deliège, A., & Nicolay, S. (2017). Analysis and indications on long-term forecasting of the oceanic Niño index with wavelet-induced components. Pure and Applied Geophysics, 174(4), 1815-1826. https://doi.org/10.1007/s00024-017-1491-4

Deser, C., & Wallace, J. M. (1987). El Niño events and their relation to the Southern Oscillation: 1925–1986. Journal of Geophysical Research: Oceans, 92(C13), 14189-14196. https://doi.org/10.1029/JC092iC13p14189

Glantz, M. H., & Ramirez, I. J. (2020). Reviewing the Oceanic Niño Index (ONI) to enhance societal readiness for El Niño’s impacts. International Journal of Disaster Risk Science, 11(3), 394-403. https://doi.org/10.1007/s13753-020-00275-w

Greenough, G., McGeehin, M., Bernard, S. M., Trtanj, J., Riad, J., & Engelberg, D. (2001). The potential impacts of climate variability and change on health impacts of extreme weather events in the United States. Environmental health perspectives, 109(suppl 2), 191-198. https://doi.org/10.1289/ehp.109-1240666

Ham, Y. G., Lee, H. J., Jo, H. S., Lee, S. G., Cai, W., & Rodrigues, R. R. (2021). Inter?Basin Interaction Between Variability in the South Atlantic Ocean and the El Niño/Southern Oscillation. Geophysical Research Letters, 48(15), e2021GL093338. https://doi.org/10.1029/2021GL093338

Hanley, D. E., Bourassa, M. A., O'Brien, J. J., Smith, S. R., & Spade, E. R. (2003). A quantitative evaluation of ENSO indices. Journal of Climate, 16(8), 1249-1258. https://doi.org/10.1175/1520-0442(2003)16%3C1249:AQEOEI%3E2.0.CO;2

Hartmann, D. L. (2015). Pacific sea surface temperature and the winter of 2014. Geophysical Research Letters, 42(6), 1894-1902. https://doi.org/10.1002/2015GL063083

Hayashi, M., Jin, F. F., & Stuecker, M. F. (2020). Dynamics for El Niño-La Niña asymmetry constrain equatorial-Pacific warming pattern. Nature communications, 11(1), 1-10. https://doi.org/10.1038/s41467-020-17983-y

Holton, J. R., & Hakim, G. J. (2013). The General Circulation. in An Introduction to Dynamic Meteorology. https://doi.org/10.1016/b978-0-12-384866-6.00010-6

Jorgensen, D. (2016). The garden and beyond: The dry season, the Ok Tedi shutdown, and the footprint of the 2015 El Niño drought. Oceania, 86(1), 25-39. https://doi.org/10.1002/ocea.5121

Kovats, R. S. (2000). El Niño and human health. Bulletin of the World Health Organization, 78, 1127-1135.

L’Heureux, M. L., Takahashi, K., Watkins, A. B., Barnston, A. G., Becker, E. J., Di Liberto, T. E., Gamble, F., Gottschalck, J., Halpert, M., Huang, B., Mosquera-Vásquez, K., & Wittenberg, A. T. (2017). Observing and predicting the 2015/16 El Niño. Bulletin of the American Meteorological Society, 98(7), 1363-1382. https://doi.org/10.1175/BAMS-D-16-0009.1

L'Heureux, M. L., & Wallace, J. M. (2006). Comparisons of ENSO indices based on in situ and satellite SST observations. Journal of Climate, 19(17), 4529-4541.

Li, Y., & Han, W. (2015). Decadal sea level variations in the Indian Ocean investigated with HYCOM: Roles of climate modes, ocean internal variability, and stochastic wind forcing. Journal of Climate, 28(23), 9143-9165. https://doi.org/10.1175/JCLI-D-15-0252.1

Lyon, B., & Camargo, S. J. (2009). The seasonally-varying influence of ENSO on rainfall and tropical cyclone activity in the Philippines. Climate Dynamics, 32(1), 125-141. https://doi.org/10.1007/s00382-008-0380-z

Merchant, C. J., Minnett, P. J., Beggs, H., Corlett, G. K., Gentemann, C., Harris, A. R., Hoyer, J., & Maturi, E. (2019). Global Sea Surface Temperature. In Taking the Temperature of the Earth. https://doi.org/10.1016/b978-0-12-814458-9.00002-2

Nachshon, U., Dragila, M., & Weisbrod, N. (2012). From atmospheric winds to fracture ventilation: Cause and effect. Journal of Geophysical Research: Biogeosciences, 117(G2). https://doi.org/10.1029/2011JG001898

Ning, J., Xu, Q., Zhang, H., Wang, T., &Fan, K. (2019). Impact of cyclonic ocean eddies on upper ocean thermodynamic response to Typhoon Soudelor. Remote Sensing, 11(8). https://doi.org/10.3390/rs11080945

O’Carroll, A. G., Armstrong, E. M., Beggs, H. M., Bouali, M., Casey, K. S., Corlett, G. K., Dash, P., Donlon, C.J., Gentemann, C.L., Høyer, J.L., & Wimmer, W. (2019). Observational needs of sea surface temperature. Frontiers in Marine Science, 6, 420. https://doi.org/10.3389/fmars.2019.00420

Philander, S. G. (1989). El Niño, La Niña, and the southern oscillation. International geophysics series, 46, X-289.

Prasetyo, Y., & Nabilah, F. (2017). Pattern Analysis of El Nino and la Nina Phenomenon Based on Sea Surface Temperature (SST) and Rainfall Intensity using Oceanic Nino Index (ONI) in West Java Area. IOP Conference Series: Earth and Environmental Science, 98(1). https://doi.org/10.1088/1755-1315/98/1/012041

Rejeki, H. A., Kunarso, K., & Munasik, M. (2018). Interannual variability of sea surface height difference between western Pacific Ocean and eastern Indian Ocean and its effect to geostrophic current in Lombok Strait. IOP Conference Series: Earth and Environmental Science, 162(1). https://doi.org/10.1088/1755-1315/162/1/012016

Rodríguez-Morata, C., Díaz, H. F., Ballesteros-Canovas, J. A., Rohrer, M., & Stoffel, M. (2019). The anomalous 2017 coastal El Niño event in Peru. Climate Dynamics, 52(9), 5605-5622. https://doi.org/10.1007/s00382-018-4466-y

Shabbar, A. (2006). The impact of El Niño-southern oscillation on the Canadian climate. Advances in Geosciences, 6, 149-153. https://doi.org/10.5194/adgeo-6-149-2006

Sonnewald, M., Wunsch, C., &Heimbach, P. (2018). Linear predictability: A sea surface height case study. Journal of Climate, 31(7), 2599–2611. https://doi.org/10.1175/JCLI-D-17-0142.1

Wang, C., & Fiedler, P. C. (2006). ENSO variability and the eastern tropical Pacific: A review. Progress in oceanography, 69(2-4), 239-266. https://doi.org/10.1016/j.pocean.2006.03.004

Wang, C., Deser, C., Yu, J. Y., DiNezio, P., & Clement, A. (2017). El Niño and southern oscillation (ENSO): a review. Coral reefs of the eastern tropical Pacific, 85-106. https://doi.org/10.1007/978-94-017-7499-4_4

Wang, F., Li, Y., & Wang, J. (2016). Intraseasonal variability of the surface zonal currents in the western tropical Pacific Ocean: Characteristics and mechanisms. Journal of Physical Oceanography, 46(12), 3639-3660. https://doi.org/10.1175/JPO-D-16-0033.1

Wang, H., Liu, K., Wang, A., Feng, J., Fan, W., Liu, Q., Xu, Y., & Zhang, Z. (2018). Regional characteristics of the effects of the El Niño-Southern Oscillation on the sea level in the China Sea. Ocean Dynamics, 68(4), 485-495. https://doi.org/10.1007/s10236-018-1144-x

Wu, Z., Wang, B., Li, J., & Jin, F. F. (2009). An empirical seasonal prediction model of the East Asian summer monsoon using ENSO and NAO. Journal of Geophysical Research: Atmospheres, 114(D18). https://doi.org/10.1029/2009JD011733

Young, I. R., & Donelan, M. A. (2018). On the determination of global ocean wind and wave climate from satellite observations. Remote Sensing of Environment, 215, 228–241. https://doi.org/10.1016/j.rse.2018.06.006

Yueh, S. H., Stiles, B. W., & Liu, W. T. (2003). QuikSCAT wind retrievals for tropical cyclones. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2616-2628. https://doi.org/10.1109/TGRS.2003.814913

Zhuang, W., Xie, S. P., Wang, D., Taguchi, B., Aiki, H., & Sasaki, H. (2010). Intraseasonal variability in sea surface height over the South China Sea. Journal of Geophysical Research: Oceans, 115(C4). https://doi.org/10.1029/2009JC005647